Materials and Textile Architecture Analyses for Mechanical Counter-Pressure Space Suits using Active Materials

Mechanical counter-pressure (MCP) space suits have the potential to improve the mobility of astronauts as they conduct planetary exploration activities. MCP suits differ from traditional gas-pressurized space suits by applying surface pressure to the wearer using tight-fitting materials rather than pressurized gas, and represent a fundamental change in space suit design. However, the underlying technologies required to provide uniform compression in a MCP garment at sufficient pressures for space exploration have not yet been perfected, and donning and doffing a MCP suit remains a significant challenge. This research effort focuses on the novel use of active material technologies to produce a garment with controllable compression capabilities (up to 30 kPa) to address these problems. We provide a comparative study of active materials and textile architectures for MCP applications; concept active material compression textiles to be developed and tested based on these analyses; and preliminary biaxial braid compression garment modeling results.United States. National Aeronautics and Space Administration (OCT Space Technology Research Fellowship Grant NNX11AM62H)MIT-Portugal Progra

Learning data-driven reduced elastic and inelastic models of spot-welded patches

Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth

Influence of temporal lobe epilepsy and temporal lobe resection on olfaction

Although temporal lobe epilepsy (TLE) and resection (TLR) impact olfactory eloquent brain structures, their influences on olfaction remain enigmatic. We sought to more definitively assess the influences of TLE and TLR using three well-validated olfactory tests and the tests’ associations with the volume of numerous temporal lobe brain structures. The University of Pennsylvania Smell Identification Test and an odor detection threshold test were administered to 71 TLE patients and 71 age- and sex-matched controls; 69 TLE patients and controls received an odor discrimination/ memory test. Fifty-seven patients and 57 controls were tested on odor identification and threshold before and after TLR; 27 patients and 27 controls were similarly tested for odor detection/discrimination. Scores were compared using analysis of variance and correlated with pre- and post-operative volumes of the target brain structures. TLE was associated with bilateral deficits in all test measures. TLR further decreased function on the side ipsilateral to resection. The hippocampus and other structures were smaller on the focus side of the TLE subjects. Although post-operative volumetric decreases were evident in most measured brain structures, modest contralateral volumetric increases were observed in some cases. No meaningful correlations were evident pre- or post-operatively between the olfactory test scores and the structural volumes. In conclusion, we demonstrate that smell dysfunction is clearly a key element of both TLE and TLR, impacting odor identification, detection, and discrimination/memory. Whether our novel finding of significant post-operative increases in the volume of brain structures contralateral to the resection side reflects plasticity and compensatory processes requires further study

Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors.

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes.

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n = 20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains raw EEG and behavioral data, pre-processed EEG of single trials in each condition, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities